Graphical-statistical method to explore variability of hydrological time series

Due to increasing concern on developing measures for predictive adaptation to climate change impacts on hydrology, several studies have tended to be conducted on trends in climatic data. Conventionally, trend analysis comprises testing the null hypothesis H-0 (no trend) by applying the Mann-Kendall or Spearman's rho test to the entire time series. This leads to lack of information about hidden short-durational increasing or decreasing trends (hereinafter called sub-trends) in the data. Furthermore, common trend tests are purely statistical in nature and their results can be meaningless sometimes, especially when not supported by graphical exploration of changes in the data. This paper presents a graphical-statistical methodology to identify and separately analyze sub-trends for supporting attribution of hydrological changes. The method is based on cumulative sum of differences between exceedance and non-exceedance counts of data points. Through the method, it is possible to appreciate that climate variability comprises large-scale random fluctuations in terms of rising and falling hydro-climatic sub-trends which can be associated with certain attributes. Illustration on how to apply the introduced methodology was made using data over the White Nile region in Africa. Links for downloading a tool called CSD-VAT to implement the presented methodology were provided.

Automated summary

This paper presents a graphical-statistical methodology to identify and separately analyze sub-trends in hydro-climatic data for supporting attribution of hydrological changes. The method is based on calculating the cumulative sum of differences between exceedance and non-exceedance counts of data points. An example using data from the White Nile region in Africa is provided to illustrate how the methodology can be applied.